**Job Description**
This Ph.D. project focuses on developing and applying computational tools, machine learning, and AI approaches for the early detection and deconstruction of chromosomal instability (CIN) in cancer. The research aims to leverage single-cell DNA sequencing and imaging data to understand CIN heterogeneity, improve detection of CIN cancers, and build models to enhance patient survival and treatment outcomes. The student will have the flexibility to focus their interests towards cancer genomics methods or early detection in imaging, with opportunities for cross-cutting research.
**Skills & Abilities**
• Highly motivated and independent.
• Strong analytical skills.
• Strong desire to develop novel computational methods and ML/AI tools to address challenging problems related to early detection and chromosomal instability in cancer.
**Qualifications**
Required Degree(s) in:
• Computational Biology
• Mathematics
• Computer Science
• Relevant biological degrees (with sufficient computational background)
**Experience**
Other:
• Relevant research experience, gained through Master’s study or while working in a laboratory, is strongly encouraged.
• Hold or expect to gain a First/Upper Second Class degree (or equivalent) in a relevant subject from any recognised university worldwide.
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